Optimization-based methods require accurate structure information of the scene as priors and use sub-pixel information to enhance the resolution of LF images. Bishop et al. [1]} formulated a variantional Bayesian framework for LF image SR based on the estimated scene disparity. Mitra and Veeraraghavan [2]} used a Gaussian mixture model to achieve LF image SR with a patch-based approach. Wanner et al. [3]}, [4]} exploited the structure tensor into a variational framework for LF image SR. Farrugia et al. [5]} developed an example-based algorithm on the patch-volumes. The LR-HR patch-volumes are decomposed into several subspaces, and a linear projection function is learned to super-resolve LF images. Rossi and Frossard [6]} developed an LF image SR approach based on a graph regularizer, which enforced the LF geometric structure with a roughly estimated disparity. Alain et al. [7]} employed an LFBM5D filter to achieve LF image SR based on SRBM3D [8]}. However, limited by the accuracy of disparity and representation ability of handcrafted features, the performance of traditional methods is not competitive.
